| --- |
| language: |
| - en |
| license: cc-by-4.0 |
| task_categories: |
| - object-detection |
| tags: |
| - traffic-signs |
| - autonomous-driving |
| - adas |
| - computer-vision |
| - yolo |
| - yolov12 |
| pretty_name: Street Sign Set |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| <div align="center"> |
|
|
| # Street Sign Set |
|
|
| []([LICENSE](https://creativecommons.org/licenses/by/4.0/)) |
| [](https://doi.org/10.34740/KAGGLE/DS/8410752) |
|
|
| [](https://www.kaggle.com/datasets/ferrantealessandro/street-sign-set) |
| [](https://huggingface.co/datasets/AlessandroFerrante/StreetSignSet) |
| [](https://universe.roboflow.com/alessandros-workspace/street-sign-set-xzdde) |
|
|
| [](https://github.com/ultralytics/ultralytics) |
| [](https://www.kaggle.com/datasets/ferrantealessandro/street-sign-set) |
| [](https://www.kaggle.com/datasets/ferrantealessandro/street-sign-set) |
|
|
|
|
| [](https://github.com/AlessandroFerrante/StreetSignSense/) |
| [](https://alessandroferrante.github.io/StreetSignSense/report/Report.pdf) |
|
|
| ----- |
|
|
| <a href='https://ko-fi.com/I6M022U0D7' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi2.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> |
|
|
| ------ |
|
|
| ### High-Quality Traffic Sign Detection Dataset |
|
|
|
|
| </div> |
|
|
| ## 📂 Dataset Overview |
|
|
| **Street Sign Set** is a comprehensive dataset designed for road sign detection in realistic contexts. It serves as the foundation for the StreetSignSense project, enabling robust detection in diverse environmental conditions. |
|
|
| The dataset is not perfectly balanced, reflecting the real-world frequency where some signs appear much more often than others. |
|
|
| ### 📊 Dataset Statistics |
|
|
| * **Total Images:** **> 7,300** images. |
| * **Classes:** **63** distinct classes. |
| * **Macro-Categories:** 5 (Priority, Prohibition, Information, Warning, Mandatory). |
| * **Format:** Standard YOLO annotations (`.txt`). |
|
|
| ## 🏷️ Class Structure and Labels |
|
|
| The 63 classes are organized into **5 macro-categories** that define the label prefix: |
|
|
| 1. **prio** (Priority) - e.g., `prio_give_way`, `stop` |
| 2. **forb** (Prohibition) - e.g., `forb_speed_over_50` |
| 3. **info** (Information) - e.g., `info_parking` |
| 4. **warn** (Warning) - e.g., `warn_right_curve` |
| 5. **mand** (Mandatory) - e.g., `mand_pass_left_right` |
|
|
| ### Primary Targets (23 Main Classes) |
|
|
| The dataset focuses on 23 main classes identified as primary targets, including: |
|
|
| * **Speed limits:** 14 classes (e.g., 5–130 km/h). |
| * **Prohibition signs:** 4 classes (e.g., no stopping/parking, no overtaking). |
| * **Priority signs:** 2 classes (e.g., give way, stop). |
| * **Curves and crossings:** 3 classes (e.g., dangerous curves, pedestrian crossing). |
|
|
| ## 🛠️ Hybrid Origin and Construction |
|
|
| This dataset is a result of a hybrid curation process: |
|
|
| * **Base:** ~4000 images sourced from existing Kaggle datasets. |
| * **Expansion:** ~3000 images manually integrated from external sources and street mapping services to cover underrepresented classes. These were manually labeled to ensure quality. |
|
|
| ## ⚙️ Technical Specifications |
|
|
| * **Filename Scheme:** Rigorous logical scheme `class_name-n.jpg` (e.g., `prio_give_way-12.jpg`). |
| * **Selective Data Augmentation:** Applied **only** to rare classes to mitigate class imbalance. Techniques include: |
| * Hue/Saturation/Brightness variations. |
| * Grayscale (23% probability). |
| * Blur and Noise simulation for adverse conditions. |
|
|
| ## 📥 Download & Access |
|
|
| To keep the GitHub repository lightweight, the raw dataset is hosted on external platforms specialized for data versioning. |
|
|
| <a href='https://ko-fi.com/I6M022U0D7' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi2.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> |
|
|
| ## 🖊️ Citation |
|
|
| If you use this dataset in your research, please cite it as follows: |
|
|
| ``` |
| @misc{alessandro_ferrante_2025, |
| title={Street Sign Set}, |
| url={[https://www.kaggle.com/ds/8410752](https://www.kaggle.com/ds/8410752)}, |
| DOI={10.34740/KAGGLE/DS/8410752}, |
| publisher={Kaggle}, |
| author={Alessandro Ferrante}, |
| year={2025} |
| } |
| ``` |
|
|
| ## Dataset Structure |
|
|
| The data is organized following the standard YOLO convention, making it ready for immediate training: |
|
|
| ```text |
| . |
| ├── train/ |
| │ ├── images/ # Training set |
| │ └── labels/ # YOLO annotations |
| ├── val/ |
| │ ├── images/ # Validation set |
| │ └── labels/ # YOLO annotations |
| ├── test/ |
| │ ├── images/ # Test set for final evaluation |
| │ └── labels/ # YOLO annotations |
| ├── data.yaml # Dataset configuration file (classes names) |
| └── dataset_analysis.csv # Detailed analysis of the dataset class distribution |
| ``` |
|
|
|
|
| ## 👨💻 Author |
|
|
| [Alessandro Ferrante](https://alessandroferrante.net) |
|
|
| Email: [streetsignsense@alessandroferrante.net](mailto:streetsignsense@alessandroferrante.net) |
|
|
